Urban Flood Modelling Research Articles

Responses of urban flood processes to local land use using a high-resolution numeric model

Rapid increase of urban impervious surface, resulting from unplanned urban sprawl, leads to the frequent occurrence of urban flood disasters caused by abnormal precipitation in flood season. In this work, a coupled urban rain flood model and normal pipe network model is employed to simulate the city inundation and pipe network hydrodynamics under different local land use types. The results shows that the peak surface inundation and pipe network outlet flow after transformation were 4.36%–42.43% and 0.01%–1.34% higher than those before transformation for P ≤ 20, and its corresponding values reached 1.24%–5.59% and 27.26%–35.21% for P > 20. The change in land use types in local areas significantly impacted inundation of the whole surface under normal rainfall (P < 20), which was also verified by the water recession coefficient. In addition, land use transformation slightly affected urban inundation for P ≥ 100, which indicates that low-impact development facilities were almost ineffective in addressing inundation under extreme rainfall. A significant impact on the pipe network discharge was observed under the high rainfall return period (P ≥ 20). The recession coefficient obtained via coupled model simulations can be effectively applied in the urban flood recession process.

Study for Underpass Inundation Process Caused by Heavy Storm Using High-resolution Urban Flood Model

Study for Underpass Inundation Process Caused by Heavy Storm Using High-resolution Urban Flood Model

A machine learning approach to evaluate the spatial variability of New York City's 311 street flooding complaints

Urbanization, accompanied by the creation of roads, pavements, and sidewalks creates an environment where there is limited infiltration capacity, leaving metropolitan areas especially vulnerable during intense rain events. Furthermore, within an urban setting, there is spatial variability, as certain areas, owing to location, topography, land feature conditions, population and physical attributes or precipitation patterns, are more prone to flood damages. To detect neighborhoods with increased flood risk, crowdsourced data, which is the consolidation of eyewitness accounts, affords particular value. With an intent to understand how factors affect the spatial variability of street flooding, the Random Forest regression machine learning algorithm is employed, where the 311 street flooding reports of New York City (NYC) serve as the response, while the explanatory variables include topographic and land feature, physical and population dynamics, locational, infrastructural, and climatic influences. This study also analyzes socio-economic variables as predictors, as to allow for better insight into potential biases within the NYC 311 crowdsourced platform. It is found that catch basin complaints have overwhelmingly the greatest predictor importance, at 41%, almost sixfold higher than that of the second highest ranked predictor, slope, at 6.7%. Thus, NYC has an apparent issue with debris blocking the basins, and this may be remediated by increased cleaning efforts or public awareness to maintain clear streets, particularly during forecasted rain events. Furthermore, more than a third of the top predictors are land feature and topographical conditions, with building characteristics dominating the category. Often excluded in urban flood models, building effects, with a combined total importance of 11.7%, have greater significance than commonly considered flooding factors, such as percent impervious cover or elevation. Another major finding is the significance of the ‘commuters who drive alone’ variable, which alerts to the prospect of more reports being filed by those more affected by street flooding, as opposed to reflecting the actual occurrence of flooding (more reports being filed by those who drive on flooded roads versus those who do not). Overall, the leading contribution of this study is the identification of the top flooding factors in NYC, along with the presentation of their specific impacts towards street flooding variability among zip codes.

Hydrological Topography Data Set (HTD)— the Data Set for High Resolution 2D Urban Flood Modeling

This study investigated the demand of data for better high resolution two-dimensional (2D) hydraulic modeling and proposes a data set named "Hydrological Topography Data Set" (HTD for short) for this purpose. Derived from airborne lidar point cloud, the HTD comprises three parts: (1) High Resolution Hydraulic Three-Dimensional Features: high resolution three-dimensional vector data that preserves the locations and elevations of topography breaklines, (2) Hydraulic Digital Elevation Model: high resolution (1 × 1 m) DEM enhanced by the terrain breaklines to preserve the landscape characteristics, and (3) Hydraulic Digital Surface Model: high resolution (1 × 1 m) DSM conditioned by removing the height of vegetation and temporary objects. With HTD, hydraulic features such as drainages with 10 m of width can be identified and properly set up in numerical models, thus can help simulating and access- ing the flood in urban area more in detail. Through case studies we demonstrate how HTD can affect the results of hydraulic modeling. The results show that HTD can help modelers to construct proper mesh (or computational grids) for 2D hydraulic modeling, and pre- serve topography characteristics (i. e., shape and elevation), which improves the modeling results in terms of spatial resolution and physical rationality, while still keeping the computation efficient.

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

Climate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.

Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation

ABSTRACT This paper explores how crowdsourced social media data complements urban flood modelling to improve model performance and achieve a better classification of impacts. In addition to georeferencing flood impacts, Twitter allows monitoring the events in terms of hazards and impacts, and YouTube facilitates a retrospective analysis from audiovisual data. The analysis of 2800 tweets collected during four storm events and of almost 900 videos of the recent history of the basin, together with the implementation of a high-resolution model, contributed to the expansion of the capacity to represent the temporal and spatial scales of the problem. The complementation of crowdsourced social media data and urban modelling enhances the understanding of the flood dynamics, thus offering a framework of greater certainty for the generation of flood risk management products.

Urban storm flood simulation using improved SWMM based on K‐means clustering of parameter samples

AbstractTo address the two problems of unclear delineation of sub‐catchment and complicated and cumbersome parameter rate determination in the Storm Water Management Model (SWMM), this study proposes a rapid construction method of SWMM based on the principle of single urban functional area combined with K‐means clustering algorithm, The research area is the southern part of Jinshui District, Zhengzhou City. The Hydrological Response Unit (HRU) contains only a single urban functional area, divided by combining the natural and social attributes of the urban surface. Calibrated uncertain parameters from 76 papers were selected as samples, and the K‐means clustering algorithm was used to cluster and calculate the parameter values, to improve the SWMM model, selecting three typical rainfall runoff processes for validation application. The results show that simulated runoff is consistent with measured runoff trends, with the NSE and R2 value scores of the flow processes of the three floods above 0.86 and the, locations and numbers of flooded nodes are consistent with the actual research. This provides a new idea and technical support for the construction of urban flood models in flood prevention and mitigation. The relevant results can provide scientific decision‐making reference for urban flood forecasting and warning.

The Compound Effect of Spatial and Temporal Resolutions on the Accuracy of Urban Flood Simulation.

Flood disaster is one of the critical threats to cities. With the intellectualization tendency of Industry 4.0, refined urban flood models can effectively reproduce flood inundation scenarios and support the decision-making on the response to the flood. However, the spatiotemporal variability of rainfall and the spatial heterogeneity of the surface greatly increase the uncertainties in urban flood simulations. Therefore, it is crucial to account for spatiotemporal variability of rainfall events and grids of the model as accurately as possible to avoid misleading simulation results. This study aims to investigate the effect of temporal resolutions of rainfall and spatial resolutions of the model on urban flood modeling in small urban catchments and to explore a proper combination of spatiotemporal schemes. The IFMS Urban (integrated flood modeling system, urban) is used to construct a one-dimension and two-dimension coupled urban flood model in the typical inundated area in Dongguan, China. Based on five temporal resolutions of rainfall input and four spatial resolutions, the compound effect of spatiotemporal resolutions on the accuracy of urban flood simulations is systematically analyzed, and the variation characteristics are investigated. The results show that the finer the temporal resolution is, the higher the simulation accuracy of the maximum inundated water depth. Considering the spatial resolution, as the spatial grid becomes smaller, the relative error of the maximum inundated water depth decreases, but it also shows some nonlinear characteristics. Therefore, the smaller grid does not always mean a better simulation. The spatial resolution has a greater impact on the flood simulation accuracy than the temporal resolution. The simulation performance reaches the best when the grid interval is 100 m and the rainfall input interval is 5 min, 10 min, or 15 min. Affected by other factors such as terrain slope, the simulation accuracies under different spatiotemporal resolutions present complex nonlinear characteristics. The mechanisms of the compound effect of the spatiotemporal resolutions on the model simulation and the effect of underlying surface and topography on model simulation will be the focus of in-depth exploration for the future urban flood model.

Understanding the effects of digital elevation model resolution and building treatment for urban flood modelling

Study regionA mountain city in the coastal region of Southeast China. Study focusThis study elucidated the effect of digital elevation model (DEM) resolution on flood simulation errors and inundation depth distributions through building treatment methods and river channel definition. The optimal DEM resolution was evaluated to determine whether satisfactory inundation simulation could be achieved for urban flash flood simulation (UFFS) and fluvial overbank flood simulation (FOFS).New hydrological insights for the region: UFFS and FOFS presented opposite effects regarding inundation extent and the spatial distribution of inundation depth with coarsening DEM resolution. In UFFS, the inundation extent and inundation depth in low-lying areas of the flood propagation pathway were underestimated. This effect was attributed to the change of local flood pathway caused by different treatment of buildings at coarser DEM resolution. The simulation error was magnified considerably when the DEM resolution was greater than the building width and gaps. In FOFS, owing to the changed channel geometry at coarser resolution, the inundation extent and inundation depth were overestimated. Using a DEM resolution and computational cell size that are lower than the river width is recommended. This study illustrated that a regional-scale effect, dependent on regional flood propagation behaviour and the impact of land use and topography on the flood propagation pathway, could be observed by coarsening the DEM resolution of inundation simulations.

Urban Pluvial Flood Modeling by Coupling Raster-Based Two-Dimensional Hydrodynamic Model and SWMM

Urban flood modeling usually involves simulating drainage network runoff and overland flow. We describe a method for urban pluvial flood modeling by coupling the stormwater management model (SWMM) with a raster-based 2D hydrodynamic model, which is based on a simplified form of the shallow water equations. Then, the method is applied to a highly urbanized area in Nanjing City, China. The elevation of the raster-based 2D hydrodynamic model shows that the raster-based model has comparable capabilities to LISFLOOD-FP for surface flood modeling. The calibration and validation results of the coupled model show that the method is reliable. Moreover, simulation results under the six rainfall return periods, which include 1-, 5-, 10-, 20-, 50-, and 100-year return periods show that node overflow, water depth, and flooding area increase proportionately as the intensity of rainfall increases. Therefore, the coupling model provides a simplified and intuitive method for urban pluvial flood modeling, which can be used to detect flood-sensitive areas and elevate the capacity of urban drainage networks for urban pluvial flooding.

Parallel Calculation Method for Urban Two-Dimensional Rainfall Flood Model Based on Compute Unified Device Architecture

Parallel Calculation Method for Urban Two-Dimensional Rainfall Flood Model Based on Compute Unified Device Architecture

Urban flood response analysis for designed rainstorms with different characteristics based on a tracer-aided modeling simulation

The frequent occurrence of urban floods has severely affected urban public safety and restricted the sustainable development of the social economy. Studies have confirmed that extreme rainstorms are determining factors for the cause of urban floods. Additionally, the rainstorm pattern could be adopted to measure the temporal change in rainstorm intensity, and there may be differences in the process of urban flood, even if the rainstorm amount and return period are the same. The in-depth analysis of urban flood disaster levels in response to rainstorms with different characteristics is of substantial significance for managing unfavorable situations and implementing flood mitigation measures. Therefore, a tracer-aided urban flood model was developed to study the impact of rainstorm patterns on urban flooding. Based on the source tracking results, the direct and indirect connections were established between different areas of the urban system, their relative importance in the urban system was explored, and the response of urban flood processes to different rainstorm patterns from the perspective of flood source areas was analyzed. This study presents a modeling task for the Longkungou drainage district of Haikou City. Simulations were performed for 21 designed rainstorm events, in which the characteristic differences in rainstorm distribution are reflected in the return period, rainstorm patterns, and peak coefficients. The results indicate that in the case of the same rainstorm amount, the unimodal patterns, Modes II (middle peak) and III (late peak), are the most unfavorable situations for the flooded area. In addition, rainstorms with bimodal patterns also had different effects on the flood volume: Models VI and V had the largest and smallest flood volume, respectively. The source tracking results further showed that the formation of flood volume in the source area of the catchment is a comprehensive effect of regional characteristics and rainstorm patterns. The source area flood volume of catchments located upstream were not sensitive to the response of rainstorm patterns, and catchments in urban system with weaker drainage systems were more sensitive than that to the response of rainstorm patterns. This study provides theoretical insights for early warning of urban flooding, implementation of urban flood mitigation strategies, and flood risk management.

Laboratory modelling of urban flooding

This paper presents two datasets obtained from laboratory experiments of urban flooding in a street network performed at the University of Liège. The experimental model represents a part of a synthetic urban district that consists of three inlets, three outlets and several three- and four- branches crossroads. The following experimental data was produced: (i) dataset 1: time-series of flow depths at model inlets and time-series of discharges at model outlets for a two-branch junction model, a two-branch bifurcation model and a district model. The datasets were generated by varying the upstream and downstream boundary conditions, i.e. flooding conditions; (ii) dataset 2 includes the same data type as dataset 1 complemented by 2D surface velocity measured using the non-intrusive LSPIV technique for eight urban form configurations in the district model. The collected data enable improving the understanding of the effect of urban forms on the urban flood processes. These two datasets are valuable for validating and improving numerical or analytical models of urban flooding and may contribute to flood risk management and flood-resilient urban design.

Quantile mapping bias correction methods to IMDAA reanalysis for calibrating NCMRWF unified model operational forecasts

ABSTRACT This study focuses on assessing different quantile mapping (QM) bias correction approaches based on empirical and parametric methods to bias-correct the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data and subsequently calibrate the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model operational forecasts. The two coastal cities Chennai and Mumbai, India, are chosen here to support the Integrated Flood Warning System (IFLOWS), initiated by the Ministry of Earth Sciences, Government of India, which provides early warning and decision support during flooding. The empirical QM methods are relatively better in correcting the quantiles with calibrated precipitation close to the observed cumulative distribution at these coastal cities. However, in extreme rainfall cases, the skill of calibrated precipitation through parametric methods seems promising and suitable for flood forecasting applications. Hence, this study demonstrates QM methods’ performance and their potential in downscaling precipitation that has significant implications for urban flood models.

Evaluating the uncertainty of urban flood model using glue approach

ABSTRACT This study is an attempt to investigate and analyze uncertainty through the Glue method in parts of Tabriz, Iran, using the SWMM urban flood model. In order to quantify the uncertainty, the initial range of input parameters including curve number, impenetrability percentage, penetrability level’s coarseness coefficient, and impenetrability level’s coarseness coefficient are defined and the GLUE algorithm is used to conduct primary sampling operation from parametric space using Latin hypercube sampling method. Considering the simulation results and the observed values of synchronized events, about %20 of the total outputs and generated parameters series have been excluded as acceptable simulations. The results obtained from analyzing posterior distribution diagrams illustrated that the acceptable posterior for CN parameters are included in the same primary range for datasets. The second priority includes N prev parameter; however, parts of the primary given range have been obtained as the acceptable posterior range for %Imperv and N Imper parameters which displays the high sensitivity of these parameters. Finally, the proper ranges for the abovementioned parameters have been computed as 85.62–88.13. 0.0275–0.0325, 54.11–76.93, and 0.0118–0.0183, respectively.

Analysis of Effects of Sponge City Projects Applying the Geodesign Framework

This study aims is to verify the effects of sponge city projects focusing on the aspect of water pollutant control and urban flood control, applying the geodesign framework as an integrated planning method that can evaluate alternatives against the impacts of the designs. The study analyzed the effects of sponge city projects in Harbin, Quzhou, and Sanya, China. Three LULC scenarios are proposed based on the geodesign framework, and the spatial distribution and quantitative values are simulated by the InVEST NDR model and urban flood model study. By comparing different scenarios, the study proved the current sponge project could improve the water pollutant control capability by 11–18% and the stormwater control capability by 0.4–6.3%. If the city-wide green infrastructure network is introduced with sponge projects, the water pollutant control capability can increase by 9–15% and the stormwater control capability can increase by 0.8–2.9%. These results show that the current sponge projects can improve the city’s sustainability and be helpful strategies to fight climate change and global warming.

UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment

In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings.

A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling

Sinks configure the surface networks for overland flow processes representations during 1D hydrodynamic modelling. The excessive number of sinks detected from high-resolution DEMs can boost 1D computational costs significantly. To pursue optimal sink numbers and their optimal spatial distribution, a Volume Ratio Sink Screening (VRSS) method was developed to screen for computationally important sinks, while compensating for volume losses from removed (unimportant) sinks, such that 1D hydrodynamic modelling yields faster computing times without significant loss of accuracy. In comparison with an existing geometry-based sink screening method, we validated this method by conducting sensitivity analyses for the proposed screening criteria in three Danish case areas of distinct topographies. Two iterative procedures were programmed to assess and compare their sink screening performances in terms of sink number reductions and volume loss reductions, and a volume loss solver was developed to quantify catchment-wide volume losses in the 1D surface network. Compared to a geometry-based sink screening method, the VRSS method performs more robustly and produces more efficient reductions in the number of sinks, as well as efficient reductions in volume losses.

Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research.

A unified formula for discharge capacity of street inlets for urban flood management

Street inlets play a key role in intercepting surface runoff into underground drainage systems, which can reduce the hazard degree during urban flood events. An accurate calculation of the discharge capacity of street inlets is vitally important in numerically modelling urban floods and managing flood resilience. A series of laboratory experiments were combined with a theoretical analysis in this study to investigate the conveyance characteristics of a typical street grate inlet. A large-scale laboratory platform was constructed, consisting of an upper-layer model street and an underground measuring system installed with a V-notch weir structure. A model street inlet was used to connect the upper and underground layers, which had a complete drainage structure including: an inlet grate, a drain box, and a connection tube. A total of 78 experimental runs were conducted using this laboratory platform, and the discharges intercepted by the grate inlet were measured for a range of incoming water depths and flow velocities, where the approaching Froude number varied from 0.05 to 0.89. These experimental results showed that the drainage pattern of the street inlet was converted from weir flow to orifice flow after the drain box was fully pressurized. In addition, a power function was derived between the relative drainage velocity through the inlet and the approaching Froude number, based on the method of dimensional analysis, and a unified formula was proposed to calculate the discharge capacity of street inlets. The corresponding coefficients in the formula were calibrated using the laboratory measurements. The unified discharge capacity formula was verified using other experimental results obtained from previous studies, and the results indicated that the calculated results using the unified formula agreed well with these measurements, with the coefficients of R2 > 0.90. Therefore, the unified formula for discharge capacity of street inlets obtained from the study reported herein has a high predictive accuracy, which can be used in numerical modelling and risk management of urban floods.